Open Access

ARTICLE

Survey of Knowledge Graph Approaches and Applications

Hangjun Zhou¹, Tingting Shen^{1, *}, Xinglian Liu¹, Yurong Zhang¹, Peng Guo^{1, 2}, Jianjun Zhang³

1 Hunan University of Finance and Economics, Changsha, China.
2 University Malaysia Sabah, Kota Kinabalu, Malaysia.
3 Hunan Normal University, Changsha, China.

* Corresponding Author: Tingting Shen. Email: .

Journal on Artificial Intelligence 2020, 2(2), 89-101. https://doi.org/10.32604/jai.2020.09968

Received 01 February 2020; Accepted 13 March 2020; Issue published 15 July 2020

Abstract

With the advent of the era of big data, knowledge engineering has received extensive attention. How to extract useful knowledge from massive data is the key to big data analysis. Knowledge graph technology is an important part of artificial intelligence, which provides a method to extract structured knowledge from massive texts and images, and has broad application prospects. The knowledge base with semantic processing capability and open interconnection ability can be used to generate application value in intelligent information services such as intelligent search, intelligent question answering and personalized recommendation. Although knowledge graph has been applied to various systems, the basic theory and application technology still need further research. On the basis of comprehensively expounding the definition and architecture of knowledge graph, this paper reviews the key technologies of knowledge graph construction, including the research progress of four core technologies such as knowledge extraction technology, knowledge representation technology, knowledge fusion technology and knowledge reasoning technology, as well as some typical applications. Finally, the future development direction and challenges of the knowledge graph are prospected.

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Keywords

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